We investigate the effects of additive outliers on the least squares (LS) estimation of threshold autoregressive models. The class of generalized-M (GM) estimates for linear time series is modified and applied to non-linear threshold processes. A Monte Car10 experiment is carried out to study the ro
β¦ LIBER β¦
Robust autoregressive estimates using quadratic programming
β Scribed by G. Zioutas; L. Camarinopoulos; E. Bora Senta
- Publisher
- Elsevier Science
- Year
- 1997
- Tongue
- English
- Weight
- 759 KB
- Volume
- 101
- Category
- Article
- ISSN
- 0377-2217
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
On robust estimation of threshold autore
β
Wai-Sum Chan; Siu-Hung Cheung
π
Article
π
1994
π
John Wiley and Sons
π
English
β 690 KB
Robust estimation of nonlinear regressio
β
Sanjoy K Sinha; Christopher A Field; Bruce Smith
π
Article
π
2003
π
Elsevier Science
π
English
β 248 KB
Robust detection of tone signals by auto
β
Emin Anarim; BΓΌlent Sankur
π
Article
π
1993
π
Elsevier Science
π
English
β 456 KB
Constrained kriging using quadratic prog
β
LimiΔ, N. ;MikeliΔ, A.
π
Article
π
1984
π
Springer
π
English
β 289 KB
Robust error-in-variables estimation usi
β
In-Won Kim; Michael J. Liebman; Thomas F. Edgar
π
Article
π
1990
π
American Institute of Chemical Engineers
π
English
β 686 KB
For systems described by algebraic or differential equation models where all variables are subject to error, the error-in-variables method (EVM) for parameter estimation has been shown to be superior to standard least-squares techniques. Previous EVM algorithms were developed assuming linear (or lin
A weak convergence result useful in robu
β
Hira L. Koul
π
Article
π
1991
π
Elsevier Science
π
English
β 872 KB